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001441032 001__ 1441032
001441032 003__ OCoLC
001441032 005__ 20230309004716.0
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001441032 019__ $$a1286660082$$a1286709601$$a1286797939$$a1286922207$$a1286948466$$a1294353289
001441032 020__ $$a9783030825683$$q(electronic bk.)
001441032 020__ $$a303082568X$$q(electronic bk.)
001441032 020__ $$z9783030825676$$q(print)
001441032 020__ $$z3030825671
001441032 0247_ $$a10.1007/978-3-030-82568-3$$2doi
001441032 035__ $$aSP(OCoLC)1286622986
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001441032 050_4 $$aQE33.2.S82
001441032 08204 $$a550.72/7$$223
001441032 1001_ $$aTolosana-Delgado, Raimon,$$eauthor.
001441032 24510 $$aGeostatistics for compositional data with R /$$cRaimon Tolosana-Delgado, Ute Mueller.
001441032 264_1 $$aCham, Switzerland :$$bSpringer,$$c2021.
001441032 300__ $$a1 online resource (xxv, 259 pages) :$$billustrations
001441032 336__ $$atext$$btxt$$2rdacontent
001441032 337__ $$acomputer$$bc$$2rdamedia
001441032 338__ $$aonline resource$$bcr$$2rdacarrier
001441032 347__ $$atext file
001441032 347__ $$bPDF
001441032 4901_ $$aUse R!,$$x2197-5744
001441032 504__ $$aIncludes bibliographical references and index.
001441032 5050_ $$a1 Introduction -- 2 A review of compositional data analysis -- 3 Exploratory data analysis -- 4 Exploratory spatial analysis -- 5 Variogram Models -- 6 Geostatistical estimation -- 7 Cross-validation -- 8 Multivariate normal score transformation -- 9 Simulation -- 10 Compositional Direct Sampling Simulation -- 11 Evaluation and Postprocessing of Results -- A Matrix decompositions -- B Complete data analysis workflows -- Index.
001441032 506__ $$aAccess limited to authorized users.
001441032 520__ $$aThis book provides a guided approach to the geostatistical modelling of compositional spatial data. These data are data in proportions, percentages or concentrations distributed in space which exhibit spatial correlation. The book can be divided into four blocks. The first block sets the framework and provides some background on compositional data analysis. Block two introduces compositional exploratory tools for both non-spatial and spatial aspects. Block three covers all necessary facets of multivariate spatial prediction for compositional data: variogram modelling, cokriging and validation. Finally, block four details strategies for simulation of compositional data, including transformations to multivariate normality, Gaussian cosimulation, multipoint simulation of compositional data, and common postprocessing techniques, valid for both Gaussian and multipoint methods. All methods are illustrated via applications to two types of data sets: one a large-scale geochemical survey, comprised of a full suite of geochemical variables, and the other from a mining context, where only the elements of greatest importance are considered. R codes are included for all aspects of the methodology, encapsulated in the R package "gmGeostats", available in CRAN
001441032 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 29, 2021).
001441032 650_0 $$aGeology$$xStatistical methods.
001441032 650_0 $$aR (Computer program language)
001441032 650_6 $$aGéologie$$xMéthodes statistiques.
001441032 650_6 $$aR (Langage de programmation)
001441032 655_0 $$aElectronic books.
001441032 7001_ $$aMueller, Ute,$$eauthor.
001441032 77608 $$iPrint version:$$aTolosana-Delgado, Raimon.$$tGeostatistics for compositional data with R.$$dCham, Switzerland : Springer, 2021$$z3030825671$$z9783030825676$$w(OCoLC)1258652234
001441032 830_0 $$aUse R!,$$x2197-5744
001441032 852__ $$bebk
001441032 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-82568-3$$zOnline Access$$91397441.1
001441032 909CO $$ooai:library.usi.edu:1441032$$pGLOBAL_SET
001441032 980__ $$aBIB
001441032 980__ $$aEBOOK
001441032 982__ $$aEbook
001441032 983__ $$aOnline
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